# HG changeset patch
# User francis
# Date 1268989862 0
# Node ID f190851b2dbb556b8b80d2eed8aee9f70cc5032e
# Parent  7f671b74a3a8bb8ae17671dfdc88176eec0e20ed
Swap Final Results and conclusion

diff -r 7f671b74a3a8 -r f190851b2dbb reports/final/content/finalproduct.tex
--- a/reports/final/content/finalproduct.tex	Fri Mar 19 09:09:52 2010 +0000
+++ b/reports/final/content/finalproduct.tex	Fri Mar 19 09:11:02 2010 +0000
@@ -1,3 +1,40 @@
+
+\chapter{Final Results}
+
+With the batch command line option of the application, it is possible to
+process a large amount of images of eyes. Using this option, 108 images of eyes
+from the CASIA database were loaded into the application; the pupil, iris and eyelids were auto-detected to generate a bitcode and store it in the database. These were then compared to an alternative image of each eye 
+to check for matches.
+
+
+To further test primarily for false matches, the first three images of each eye were
+loaded into the database, and then compared to a fourth
+image for each eye. As there were originally three images for each eye, and
+then a comparison was performed on all of the extra 108 images, roughly 35,000
+iris comparisons took place ($(108 * 3) * 108$). 
+
+Our results of these tests are as follows: \begin{itemize} \item 0 false
+		matches in $\sim$35,000 comparisons \item 70\% (75 out of 108) match rate
+		with Professor Daugman's suggested 0.32 Hamming distance.
+\end{itemize}
+
+The complete lack of any false matches and the incredibly high match rate are a
+testament to the robustness and feasibility of an iris recognition system. The
+tests have suggested that in virtually any case where the iris boundary is
+correctly located, the iris should be correctly identified.
+
+As a roughly 70\% match rate was achieved for the iris location, this percentage has, as
+predicted, been reflected in the overall match rate in the
+database of images. Similarly to Daugman, we can also observe that the Hamming
+distances produced by comparing different irides tends to follow a binomial distribution, with a mean around 0.46; see
+figure \ref{hd}.
+
+\begin{figure}
+  \centering
+    \includegraphics[width=0.55\textwidth]{hd}
+    \caption{The distribution of Hamming distances for one run of all the irides in the database}
+    \label{hd}
+\end{figure}
 \chapter{Conclusions}
 \section{Final Product}
 The final prototype is a functional iris detection application, which provides the 
@@ -49,43 +86,6 @@
 thickness of the top eyelid, it can be seen that, although not perfect, a very
 reasonable estimate of the eyelid boundary is found automatically.
 
-\chapter{Final Results}
-
-With the batch command line option of the application, it is possible to
-process a large amount of images of eyes. Using this option, 108 images of eyes
-from the CASIA database were loaded into the application; the pupil, iris and eyelids were auto-detected to generate a bitcode and store it in the database. These were then compared to an alternative image of each eye 
-to check for matches.
-
-
-To further test primarily for false matches, the first three images of each eye were
-loaded into the database, and then compared to a fourth
-image for each eye. As there were originally three images for each eye, and
-then a comparison was performed on all of the extra 108 images, roughly 35,000
-iris comparisons took place ($(108 * 3) * 108$). 
-
-Our results of these tests are as follows: \begin{itemize} \item 0 false
-		matches in $\sim$35,000 comparisons \item 70\% (75 out of 108) match rate
-		with Professor Daugman's suggested 0.32 Hamming distance.
-\end{itemize}
-
-The complete lack of any false matches and the incredibly high match rate are a
-testament to the robustness and feasibility of an iris recognition system. The
-tests have suggested that in virtually any case where the iris boundary is
-correctly located, the iris should be correctly identified.
-
-As a roughly 70\% match rate was achieved for the iris location, this percentage has, as
-predicted, been reflected in the overall match rate in the
-database of images. Similarly to Daugman, we can also observe that the Hamming
-distances produced by comparing different irides tends to follow a binomial distribution, with a mean around 0.46; see
-figure \ref{hd}.
-
-\begin{figure}
-  \centering
-    \includegraphics[width=0.55\textwidth]{hd}
-    \caption{The distribution of Hamming distances for one run of all the irides in the database}
-    \label{hd}
-\end{figure}
-
 \newpage
 
 \section{Source Code}